Application of an Improved Dung Beetle Algorithm for Collaborative Optimization of Marine Ice Thermal Storage and Air Conditioning System

Objectives To address the significant load of marine air conditioning systems, this paper proposes to embed ice storage cooling technology into the air conditioning system and applies Multi-strategy Improved Dung Beetle Optimization algorithm (MIDBO) to optimize the operation strategy. Methods First, Hammersley sequence is adopted to initialize the population, improving the uniform coverage of the search space; then, Lévy flight mechanism is introduced to enhance the algorithm's jumping characteristics and global search capability; finally, an adaptive spiral search strategy is integrated to balance global exploration and local exploitation capabilities. Through multi-strategy fusion, MIDBO effectively improves the operational efficiency and economic performance of ship ice storage cooling systems. Results Simulation experiments show that MIDBO outperforms classic optimization algorithms such as GWO, PSO, and SSA in all 10 benchmark test functions, demonstrating stronger optimization ability and convergence precision. When applied to ship ice storage cooling system optimization, the MIDBO strategy reduces daily fuel consumption to 3338.6 kg, saving 36.6 kg compared to the baseline scheme. The required ice storage tank volume is only 10.77 m³, with an investment cost of 41,449.43 USD, annual cost savings of 7,171.30 USD, and a payback period of 5.20 years, significantly better than the traditional peak shaving strategy's 5.82 years and other optimization algorithms. In terms of environmental benefits, the MIDBO scheme achieves annual CO₂ emission reductions of 21.12 tons, a reduction ratio of 1.08%, higher than other algorithms.Sensitivity analysis reveals that fuel price is the dominant factor affecting system economics, with significantly greater impact than interest rate and charging temperature. Unlike traditional "peak shaving and valley filling" strategies, MIDBO achieves optimal system resource allocation by precisely controlling the load to keep diesel generators operating in their high-efficiency range whenever possible. Conclusions The proposed MIDBO algorithm can significantly optimize the operation strategy of ship ice storage cooling systems, improve energy utilization efficiency, reduce operating costs, and minimize environmental impact.